Why do LLMs often describe a bug instead of fixing it?

Vague prompts invite vague answers. This builder gives the model a strict procedure — restate the bug, find the root cause, explain it, return corrected code, list edge cases — so it commits to a concrete fix.

Do I need all the fields?

No. The more context you provide, the better the diagnosis, but the prompt adapts and only includes the sections you fill in. Even just an error message and code snippet produces a useful prompt.

Does it execute or run my code?

No. It builds the prompt locally in your browser with no API key and no network call. You run the resulting prompt in whatever LLM you use.

What if the model can't be certain of the cause?

The prompt instructs it to name the single most useful thing to check and still give a best-effort fix, rather than stalling. That keeps you moving even with incomplete context.

Why include expected versus actual behavior?

It anchors the model on the real symptom. Without it, the model may "fix" code that compiles but still does the wrong thing. Stating expected and actual outcomes targets the actual defect.

Debugging Prompt Builder

Debugging prompt builder

Ask an LLM to “look at this error” and you often get a paragraph explaining what the error means — not a fix. The debugging prompt builder structures your error message, stack trace, code, and expected-versus-actual behavior into a prompt that forces the model through a real debugging procedure and returns a specific, paste-ready fix.

How it works

You pick the language and fill in whatever context you have: the error message, the stack trace, the relevant code, and a short statement of what you expected versus what happened. The tool assembles a prompt that tells the model to work in order — restate the bug, pinpoint the root cause with line references, explain why it produces the symptom, provide corrected code ready to paste, and list the edge cases the fix now covers and how to verify it. Empty fields are omitted automatically. It all runs locally; no API key is required.

Tips and notes

Include the full stack trace. The top frame is the symptom; the frames below it usually point at the cause.
State expected versus actual. This is what separates “fixed the crash” from “fixed the behavior” — give the model the real target.
Trim the code to what matters. Paste the function plus anything it calls that could be implicated, not the whole file.
Use it to write a failing test next. Once the model names the root cause, pair this with the test-generation builder to lock the fix in place.